| Federated multi-task learning V Smith, CK Chiang, M Sanjabi, AS Talwalkar Advances in neural information processing systems 30, 2017 | 2189 | 2017 |
| Online optimization with gradual variations CK Chiang, T Yang, CJ Lee, M Mahdavi, CJ Lu, R Jin, S Zhu Conference on Learning Theory, 6.1-6.20, 2012 | 264 | 2012 |
| An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits P Auer, CK Chiang Conference on Learning Theory, 116-120, 2016 | 134 | 2016 |
| Pareto front identification from stochastic bandit feedback P Auer, CK Chiang, R Ortner, M Drugan Artificial intelligence and statistics, 939-947, 2016 | 51 | 2016 |
| Beating bandits in gradually evolving worlds CK Chiang, CJ Lee, CJ Lu Conference on Learning Theory, 210-227, 2013 | 17 | 2013 |
| Hyper-parameter tuning under a budget constraint Z Lu, CK Chiang, F Sha arXiv preprint arXiv:1902.00532, 2019 | 16 | 2019 |
| Pseudo-reward algorithms for contextual bandits with linear payoff functions KC Chou, HT Lin, CK Chiang, CJ Lu Asian Conference on Machine Learning, 344-359, 2015 | 11 | 2015 |
| Online learning with queries CK Chiang, CJ Lu Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete …, 2010 | 4 | 2010 |
| Resisting dynamic strategies in gradually evolving worlds CJ Lee, CK Chiang, ME Wu 2015 Third International Conference on Robot, Vision and Signal Processing …, 2015 | 1 | 2015 |
| Online Learning Problems against Dynamic Strategies in Gradually Evolving Worlds. CJ Lee, CK Chiang, ME Wu J. Inf. Hiding Multim. Signal Process. 8 (4), 869-879, 2017 | | 2017 |
